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Python Data Loading from salesforce to snowflake with dlt

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This page provides technical documentation on how to load data from Salesforce, a cloud platform that optimizes business operations and customer relationship management, to Snowflake, a cloud-based data warehousing platform designed for storing, processing, and analyzing large data volumes. The process is facilitated using an open-source Python library named dlt. Further details on Salesforce can be found at The dlt library serves as a bridge, enabling efficient data transfer between Salesforce and Snowflake, thus streamlining your data management tasks.

dlt Key Features

  • Salesforce Integration: dlt provides a verified source for Salesforce, enabling seamless data extraction and loading from Salesforce API to your chosen destination. Learn more about it here.
  • Snowflake Destination: dlt supports Snowflake as a destination, allowing data to be loaded into Snowflake data warehouse efficiently. Detailed setup instructions and authentication types can be found here.
  • Data Lineage and Schema Lineage: dlt supports data and schema lineage, facilitating traceability and understanding of how data moves and transforms within your data stack. Read more about this here.
  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This promotes data consistency, traceability, and control throughout the data processing lifecycle. More on this can be found here.
  • Scalable Data Extraction: dlt leverages iterators, chunking, and parallelization for scalable data extraction. It also utilizes implicit extraction DAGs for efficient API calls for data enrichments or transformations. Learn more about this here.

Getting started with your pipeline locally

0. Prerequisites

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt

First you need to install the dlt library with the correct extras for Snowflake:

pip install "dlt[snowflake]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Salesforce to Snowflake. You can run the following commands to create a starting point for loading data from Salesforce to Snowflake:

# create a new directory
mkdir salesforce_pipeline
cd salesforce_pipeline
# initialize a new pipeline with your source and destination
dlt init salesforce snowflake
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── salesforce/ # folder with source specific files
│ └── ...
├── # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)

2. Configuring your source and destination credentials

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml

# put your configuration values here

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
dlthub_telemetry = true

generated secrets.toml

# put your secret values and credentials here. do not share this file and do not push it to github

user_name = "user_name" # please set me up!
password = "password" # please set me up!
security_token = "security_token" # please set me up!

database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
warehouse = "warehouse" # please set me up!
role = "role" # please set me up!

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations
  • Read more about setting up the Salesforce source in our docs.
  • Read more about setting up the Snowflake destination in our docs.

3. Running your pipeline for the first time

The dlt cli has also created a main pipeline script for you at, as well as a folder salesforce that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:

#!/usr/bin/env python3
"""Pipeline to load Salesforce data."""
import dlt
from salesforce import salesforce_source

def load() -> None:
"""Execute a pipeline from Salesforce."""

pipeline = dlt.pipeline(
pipeline_name="salesforce", destination='snowflake', dataset_name="salesforce_data"
# Execute the pipeline
load_info =

# Print the load info

if __name__ == "__main__":

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:


4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline salesforce info

You can also use streamlit to inspect the contents of your Snowflake destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline salesforce show

5. Next steps to get your pipeline running in production

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to

  • Deploy with Github Actions: dlt allows you to deploy your pipelines using Github Actions. This provides a seamless integration with your Github repository and allows for continuous integration and deployment.
  • Deploy with Airflow: You can also deploy your dlt pipelines with Airflow. This is especially useful if you are already using Airflow for your data pipelines.
  • Deploy with Google Cloud Functions: If you are using Google Cloud for your infrastructure, dlt provides a way to deploy your pipelines using Google Cloud Functions. This allows you to take advantage of the scalability and reliability of Google Cloud.
  • Other Deployment Options: dlt is flexible and supports various other deployment options. You can find more information about these in the deployment documentation.

The running in production section will teach you about:

  • Monitor Your Pipeline: dlt provides tools for monitoring your pipeline's performance and status. This includes tracking the progress of data loading, checking the status of each job, and exploring the load history. More details can be found here.
  • Set Up Alerts: With dlt, you can set up alerts to get notified of any changes or issues in your pipeline. This includes alerts for schema changes, failed jobs, and more. Check out the guide on how to set up alerts here.
  • Set Up Tracing: Tracing in dlt allows you to track the execution of your pipeline, providing detailed insights into the extract, normalize, and load steps. This can be especially useful for debugging and optimization. Learn how to set up tracing here.

Available Sources and Resources

For this verified source the following sources and resources are available

Source salesforce

"Salesforce source provides comprehensive business data, covering customer details, sales opportunities, product pricing, and marketing campaigns."

Resource NameWrite DispositionDescription
accountmergeRepresents an individual or organization that interacts with your business
campaignreplaceRepresents a marketing initiative or project designed to achieve specific goals
contactreplaceRepresents an individual person associated with an account or organization
leadreplaceRepresents a prospective customer/individual/org. that has shown interest in a company's products/services
opportunitymergeRepresents a sales opportunity for a specific account or contact
pricebook_2replaceUsed to manage product pricing and create price books
pricebook_entryreplaceRepresents a specific price for a product in a price book
product_2replaceUsed for managing and organizing your product-related data within the Salesforce ecosystem
sf_userreplaceRepresents an individual who has access to a Salesforce org or instance
user_rolereplaceRepresents a role within the organization's hierarchy

Additional pipeline guides

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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